Field of the Invention
The present invention generally relates to methods for performing secure multiparty computations.
Description of the Related Art
Whether in business, government, or the military, information can be exploited to gain a competitive advantage. Each and every day, more and more information is exploited to make systems more efficient, more accurate, and more reliable. Consider two simple examples. A product's reputation, as determined by other consumer's feedback, is an often used measure when one is purchasing products online. This sort of information has become almost ubiquitous in online purchasing. A similar feedback mechanism is also used in a number of distributed systems in a more decentralized fashion. When one node, say nq, needs to interact with another, say nt, nq can query its neighbors to find out how much they trust nt and therefore come up with a reputation for nt.
Another example is in the case of a smart grid. A smart grid is a modernized power grid that enables bidirectional flows of energy and utilizes two-way communication and control capabilities that may lead to an array of new functionalities and applications. By definition the smart grid may exploit fine granular data from consumers, suppliers, etc. to greatly enhance the grid. A common scenario in the smart grid may be to have each household meter report their instantaneous usage back to the supplier. This information may then be exploited at a supplier or distributor to better optimize generation, distribution, or to aid in purchasing or selling excess production.
However, this increased gathering and use of information comes with tradeoffs. Often in these cases, it is privacy that is traded in order to achieve the benefits set out above. In the case of reputation, compromised privacy may incentivize untruthfulness when providing feedback in order to avoid retribution, for example. In this case, the compromise of privacy may diminish the utility of the entire system. In the case of the smart grid, the gathered information may leak other, unintended information. This other, unintended information may be used to determine whether or not someone is home, how many people live in a house, what appliances are in use, etc.
Researchers have, for decades, been developing methods to deal with these problems. The most promising technique can broadly be called privacy preserving computation. It stems from the observation that, in many applications, interested parties really need not learn the inputs of the other participants. What interested parties really need is some function of those inputs. In both of the example applications outlined previously, an interested party really only needs to learn statistics on the inputs (e.g., mean or standard deviation). Thus, what is needed in the art is a mechanism that allows for better practice of the principle of least privilege, i.e., that a node should be given access to only the minimal amount of information necessary to do its job.